We investigate the association between task difficulty and crowdworker performance.

Crowdsourcing experiments

We cooperate with the Institute of Community Medicine, University Medicine Greifswald

Publications:

• Anne Rother, Uli Niemann, Tommy Hielscher, Henry Völzke, Till Ittermann, and Myra Spiliopoulou. Assessing the difficulty of annotating medical data in crowdworking with help of experiments. PLOS ONE, (16)7:1-26, Public Library of Science, July 2021.

• Neetha Jambigi, Tirtha Chanda, Vishnu Unnikrishnan, and Myra Spiliopoulou. Assessing the Difficulty of Labelling an Instance in Crowdworking.. 2nd Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning@ ECML PKDD 2020, 2020.

• Stefan Räbiger, Gizem Gezici, Yücel Saygın, and Myra Spiliopoulou. Predicting worker disagreement for more effective crowd labeling. 2018 IEEE 5th International Conference on Data Science andAdvanced Analytics (DSAA), 179--188, 2018.

• Stefan Räbiger, Myra Spiliopoulou, and Yücel Saygına. How do annotators label short texts? Toward understanding the temporal dynamics of tweet labeling. Information Sciences, (457–458):29-47, 2018.

• Stefan Räbiger, and Myra Spiliopoulou. A framework for validating the merit of properties that predict the influence of a twitter user. Expert Systems with Applications, (42)5:2824-2834, 2015.

Experiments on human learning

Cooperation with Leibniz Institute of Neurobiology Magdeburg

Together with the Leibniz Institute of Neurobiology Magdeburg, we analyze experiments on human learning. We also cooperate in the context of the studies profile "Learning Systems" of the bachelor degree Informatik.

Publications:

• Amir Abolfazli, André Brechmann, Susann Wolff, and Myra Spiliopoulou. Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment. Scientific reports, (10)1:1--12, Nature Publishing Group, 2020.